MoHex 2.0: A Pattern-Based MCTS Hex Player

By Shih-Chieh Huang, Broderick Arneson, Ryan B. Hayward, Martin Müller, Jakub Pawlewicz. 2013

54 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Sociology and Political Science, Reinforcement Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. David Barber
2. Zheng Tian
3. Thomas W. Anthony
4. Andrew Jones
5. Hector Geffner
6. James Goodman
7. Yoshua Bengio
8. Kyunghyun Cho

In Economics and Econometrics

1. David Barber
2. Zheng Tian
3. Thomas W. Anthony
4. James Goodman
5. Hector Geffner
6. Noah D. Goodman
7. Eric Zelikman
8. Byron Boots

In Reinforcement Learning

1. David Barber
2. Zheng Tian
3. Thomas W. Anthony
4. Andrew Jones

Citations in the corpus

• Thinking Fast and Slow with Deep Learning and Tree Search (2017, background): “MOHEX [8] is the state-of-the-art Hex player; versions of it have won every Computer Games Olympiad Hex tournament since 2009.”
• Scaling Scaling Laws with Board Games (2021, background): “In fact, despite sustained attention from games researchers [19]–[21], computers only surpassed human-level play at large board sizes in 2020 [22].”